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Machine learning paradigmsadvances i...

Jain, Lakhmi C.

Machine learning paradigmsadvances in data analytics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning paradigmsedited by George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain.
其他題名:	advances in data analytics /
其他作者:	Tsihrintzis, George A.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xvi, 370 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
電子資源:	http://dx.doi.org/10.1007/978-3-319-94030-4
ISBN:	9783319940304$q(electronic bk.)

Machine learning paradigmsadvances in data analytics /
Machine learning paradigmsadvances in data analytics /[electronic resource] :edited by George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain. - Cham :Springer International Publishing :2019. - xvi, 370 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1491868-4394 ;. - Intelligent systems reference library ;v.24..

Data Analytics in the Medical, Biological and Signal Sciences -- Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics -- Classification Methods in Image Analysis with a Special Focus on Medical Analytics -- Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field -- Machine Learning Methods for the Protein Fold Recognition Problem.

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

ISBN: 9783319940304$q(electronic bk.)

Standard No.: 10.1007/978-3-319-94030-4doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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520 $a This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.
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